Scene classification for place recognition

A non-transitory computer-readable recording medium storing a computer program therein, the computer program, when executed by one or more computing devices, causing the one or more computing devices to perform a training method for classifying an image, the method comprising:

• providing, by the one or more computing devices, a set of training images, each training image being labeled to indicate whether it is a place type;
• performing, by the one or more computing devices, measurements of image data for each image in the set of training images to derive a result, the result comprising a series of variables for each training image in the set;
• for each training image, determining by the one or more computing devices and based at least in part on the series of variables, one or more measurement weights and one or more measurement thresholds;
• and
• adjusting, by the one or more computing devices, the one or more measurement weights and one or more measurement thresholds to set a false positive threshold and a false negative threshold for identifying whether an actual image is of the place type;
• performing, by the one or more computing devices, a first classification of the actual image based on at least one of the false positive threshold and the false negative threshold using only metadata of the actual image to determine whether the actual image is of the place type;
• performing, by the one or more computing devices, a second classification of the actual image based on at least one of the false positive threshold and the false negative threshold using both the metadata and information about the actual image to determine whether the actual image is of the place type;
• and
• when neither the first nor the second classification determines that the actual image is of the place type, then the method further comprises performing filtering to determine whether the actual image is of an object or a person.

A system for training classifying of imagery, the system comprising:

• memory storing a set of training images, each training image being labeled to indicate whether it is a place type;
• one or more computing devices coupled to the memory, the one or more computing devices configured to:
• perform measurements of image data for each image in the set of training images to derive a result, the result comprising a series of variables for each training image in the set;
• for each training image, determining, based at least in part on the series of variables, one or more measurement weights and one or more measurement thresholds;
• and
• adjust the one or more measurement weights and one or more measurement thresholds to set a false positive threshold and a false negative threshold for identifying whether an actual image is of the place type;
• perform a first classification of the actual image based on at least one of the false positive threshold and the false negative threshold using only metadata of the actual image to determine whether the actual image is of the place type;
• perform a second classification of the actual image based on at least one of the false positive threshold and the false negative threshold using both the metadata and information about the actual image to determine whether the actual image is of the place type;
• and
• when neither the first nor the second classification determines that the actual image is of the place, then the one or more computing devices are further configured to perform filtering to determine whether the actual image is of an object or a person.